import calliope
# We increase logging verbosity
calliope.set_log_verbosity('INFO', include_solver_output=False)
model = calliope.examples.urban_scale()
[2022-02-07 16:53:31] INFO Model: initialising [2022-02-07 16:53:31] INFO Model: preprocessing stage 1 (model_run) [2022-02-07 16:53:32] INFO Model: preprocessing stage 2 (model_data) [2022-02-07 16:53:32] INFO Model: preprocessing complete
# Model inputs can be viewed at `model.inputs`.
# Variables are indexed over any combination of `techs`, `locs`, `carriers`, `costs` and `timesteps`,
# although `techs`, `locs`, and `carriers` are often concatenated.
# e.g. `chp`, `X1`, `heat` -> `X1::chp::heat`
model.inputs
<xarray.Dataset>
Dimensions: (loc_techs_supply_plus: 3, loc_techs: 26, loc_techs_finite_resource: 9, timesteps: 48, loc_techs_area: 3, loc_techs_export: 4, costs: 1, loc_techs_investment_cost: 20, loc_techs_om_cost: 9, loc_techs_transmission: 10, locs: 4, coordinates: 2, techs: 9, carrier_tiers: 3, loc_tech_carriers_conversion_plus: 3, loc_carriers: 10, loc_techs_non_conversion: 23, loc_techs_conversion: 2, loc_techs_conversion_plus: 1, carriers: 3)
Coordinates: (12/20)
* carrier_tiers (carrier_tiers) <U5 'in' 'out_2' 'out'
* carriers (carriers) <U11 'electricity' ... '...
* coordinates (coordinates) object 'x' 'y'
* costs (costs) object 'monetary'
* loc_carriers (loc_carriers) object 'X3::gas' ......
* loc_tech_carriers_conversion_plus (loc_tech_carriers_conversion_plus) object ...
... ...
* loc_techs_om_cost (loc_techs_om_cost) object 'X1::sup...
* loc_techs_supply_plus (loc_techs_supply_plus) object 'X1:...
* loc_techs_transmission (loc_techs_transmission) object 'X1...
* locs (locs) object 'X1' 'N1' 'X3' 'X2'
* techs (techs) object 'demand_heat' ... 'h...
* timesteps (timesteps) datetime64[ns] 2005-07-...
Data variables: (12/38)
parasitic_eff (loc_techs_supply_plus) float64 0.8...
energy_cap_max (loc_techs) float64 250.0 ... 2e+03
resource_eff (loc_techs_finite_resource) float64 ...
energy_con (loc_techs) float64 nan 1.0 ... nan
force_resource (loc_techs_finite_resource) bool Tr...
resource (loc_techs_finite_resource, timesteps) float64 ...
... ...
lookup_loc_techs_conversion_plus (carrier_tiers, loc_techs_conversion_plus) object ...
lookup_loc_techs_export (loc_techs_export) object 'X1::pv::...
lookup_loc_techs_area (locs) object 'X1::pv' '' ... 'X2::pv'
timestep_resolution (timesteps) float64 1.0 1.0 ... 1.0
timestep_weights (timesteps) float64 1.0 1.0 ... 1.0
max_demand_timesteps (carriers) datetime64[ns] 2005-07-0...
Attributes:
calliope_version: 0.6.8-dev
applied_overrides:
scenario: None
defaults: available_area: null\ncarrier_ratios: null\ncharge_r...
allow_operate_mode: 1array(['in', 'out_2', 'out'], dtype='<U5')
array(['electricity', 'heat', 'gas'], dtype='<U11')
array(['x', 'y'], dtype=object)
array(['monetary'], dtype=object)
array(['X3::gas', 'X3::heat', 'X2::electricity', 'N1::heat', 'X2::heat',
'X1::heat', 'X1::electricity', 'X2::gas', 'X3::electricity', 'X1::gas'],
dtype=object)array(['X1::chp::electricity', 'X1::chp::gas', 'X1::chp::heat'], dtype=object)
array(['X1::pv', 'X1::power_lines:X3', 'X3::demand_electricity',
'N1::heat_pipes:X2', 'X2::pv', 'X1::chp', 'X2::heat_pipes:N1',
'X3::boiler', 'X3::pv', 'X2::power_lines:X1', 'X2::demand_heat',
'X1::supply_grid_power', 'X1::heat_pipes:N1', 'X2::boiler',
'X2::demand_electricity', 'X3::heat_pipes:N1', 'X3::demand_heat',
'X3::power_lines:X1', 'X1::demand_electricity', 'X1::power_lines:X2',
'X1::supply_gas', 'X2::supply_gas', 'X1::demand_heat',
'N1::heat_pipes:X3', 'N1::heat_pipes:X1', 'X3::supply_gas'],
dtype=object)array(['X1::pv', 'X3::pv', 'X2::pv'], dtype=object)
array(['X3::boiler', 'X2::boiler'], dtype='<U10')
array(['X1::chp'], dtype='<U7')
array(['X1::pv', 'X1::chp', 'X3::pv', 'X2::pv'], dtype=object)
array(['X2::demand_electricity', 'X3::demand_heat', 'X1::pv',
'X1::demand_electricity', 'X3::demand_electricity', 'X2::pv', 'X3::pv',
'X1::demand_heat', 'X2::demand_heat'], dtype=object)array(['X1::pv', 'X1::power_lines:X3', 'N1::heat_pipes:X2', 'X2::pv',
'X1::chp', 'X2::heat_pipes:N1', 'X3::boiler', 'X3::pv',
'X2::power_lines:X1', 'X1::supply_grid_power', 'X1::heat_pipes:N1',
'X2::boiler', 'X3::heat_pipes:N1', 'X3::power_lines:X1',
'X1::power_lines:X2', 'X1::supply_gas', 'X2::supply_gas',
'N1::heat_pipes:X3', 'N1::heat_pipes:X1', 'X3::supply_gas'],
dtype=object)array(['X1::pv', 'X1::power_lines:X3', 'X3::demand_electricity',
'N1::heat_pipes:X2', 'X2::pv', 'X2::heat_pipes:N1', 'X3::pv',
'X2::power_lines:X1', 'X2::demand_heat', 'X1::supply_grid_power',
'X1::heat_pipes:N1', 'X2::demand_electricity', 'X3::heat_pipes:N1',
'X3::demand_heat', 'X3::power_lines:X1', 'X1::demand_electricity',
'X1::power_lines:X2', 'X1::supply_gas', 'X2::supply_gas',
'X1::demand_heat', 'N1::heat_pipes:X3', 'N1::heat_pipes:X1',
'X3::supply_gas'], dtype=object)array(['X1::supply_grid_power', 'X2::boiler', 'X1::supply_gas', 'X2::pv',
'X2::supply_gas', 'X1::chp', 'X3::boiler', 'X3::pv', 'X3::supply_gas'],
dtype=object)array(['X1::pv', 'X3::pv', 'X2::pv'], dtype=object)
array(['X1::heat_pipes:N1', 'X3::heat_pipes:N1', 'X1::power_lines:X3',
'X1::power_lines:X2', 'N1::heat_pipes:X2', 'X2::heat_pipes:N1',
'N1::heat_pipes:X3', 'X2::power_lines:X1', 'N1::heat_pipes:X1',
'X3::power_lines:X1'], dtype=object)array(['X1', 'N1', 'X3', 'X2'], dtype=object)
array(['demand_heat', 'boiler', 'power_lines', 'supply_grid_power', 'pv',
'chp', 'demand_electricity', 'supply_gas', 'heat_pipes'], dtype=object)array(['2005-07-01T00:00:00.000000000', '2005-07-01T01:00:00.000000000',
'2005-07-01T02:00:00.000000000', '2005-07-01T03:00:00.000000000',
'2005-07-01T04:00:00.000000000', '2005-07-01T05:00:00.000000000',
'2005-07-01T06:00:00.000000000', '2005-07-01T07:00:00.000000000',
'2005-07-01T08:00:00.000000000', '2005-07-01T09:00:00.000000000',
'2005-07-01T10:00:00.000000000', '2005-07-01T11:00:00.000000000',
'2005-07-01T12:00:00.000000000', '2005-07-01T13:00:00.000000000',
'2005-07-01T14:00:00.000000000', '2005-07-01T15:00:00.000000000',
'2005-07-01T16:00:00.000000000', '2005-07-01T17:00:00.000000000',
'2005-07-01T18:00:00.000000000', '2005-07-01T19:00:00.000000000',
'2005-07-01T20:00:00.000000000', '2005-07-01T21:00:00.000000000',
'2005-07-01T22:00:00.000000000', '2005-07-01T23:00:00.000000000',
'2005-07-02T00:00:00.000000000', '2005-07-02T01:00:00.000000000',
'2005-07-02T02:00:00.000000000', '2005-07-02T03:00:00.000000000',
'2005-07-02T04:00:00.000000000', '2005-07-02T05:00:00.000000000',
'2005-07-02T06:00:00.000000000', '2005-07-02T07:00:00.000000000',
'2005-07-02T08:00:00.000000000', '2005-07-02T09:00:00.000000000',
'2005-07-02T10:00:00.000000000', '2005-07-02T11:00:00.000000000',
'2005-07-02T12:00:00.000000000', '2005-07-02T13:00:00.000000000',
'2005-07-02T14:00:00.000000000', '2005-07-02T15:00:00.000000000',
'2005-07-02T16:00:00.000000000', '2005-07-02T17:00:00.000000000',
'2005-07-02T18:00:00.000000000', '2005-07-02T19:00:00.000000000',
'2005-07-02T20:00:00.000000000', '2005-07-02T21:00:00.000000000',
'2005-07-02T22:00:00.000000000', '2005-07-02T23:00:00.000000000'],
dtype='datetime64[ns]')array([0.85, 0.85, 0.85])
array([ 250., 2000., nan, 2000., 250., 1500., 2000., 600., 50.,
2000., nan, 2000., 2000., 600., nan, 2000., nan, 2000.,
nan, 2000., 2000., 2000., nan, 2000., 2000., 2000.])array([nan, nan, 1., nan, nan, 1., 1., nan, nan])
array([nan, 1., 1., 1., nan, 1., 1., 1., nan, 1., 1., nan, 1.,
1., 1., 1., 1., 1., 1., 1., nan, nan, 1., 1., 1., nan])array([ True, True, True, True, True, True, True, True, True])
array([[-9.45458012e+01, -7.69606192e+01, -7.74757501e+01,
-7.74757501e+01, -8.27314956e+01, -1.48533479e+02,
-1.89570817e+02, -2.38734711e+02, -2.44284493e+02,
-2.31440181e+02, -2.22530771e+02, -2.22593033e+02,
-2.04439029e+02, -1.96128922e+02, -2.04488194e+02,
-2.25942018e+02, -1.93132717e+02, -2.59507784e+02,
-2.49024872e+02, -2.60251628e+02, -2.54605599e+02,
-2.05449509e+02, -1.35232572e+02, -9.47389753e+01,
-1.02582511e+02, -8.58988086e+01, -8.43534158e+01,
-8.66715049e+01, -9.08969886e+01, -1.47760782e+02,
-1.99099472e+02, -2.38777508e+02, -2.35119872e+02,
-2.31648466e+02, -2.27171841e+02, -2.27213317e+02,
-1.99838408e+02, -1.91537233e+02, -1.99895515e+02,
-2.21324570e+02, -1.88344877e+02, -2.49962248e+02,
-2.48894106e+02, -2.69344347e+02, -2.45412357e+02,
-1.96280957e+02, -1.35289242e+02, -1.03741556e+02],
[-1.56000000e-02, -8.60322027e-01, -1.56000000e-02,
-1.56000000e-02, -8.60406948e-01, -7.26332670e+00,
-9.39822908e+00, -5.79284175e+00, -3.32258511e+00,
-1.92726389e+00, -1.62161880e+00, -1.37951739e+00,
...
-5.28681742e-01, -4.87107778e-01, -4.91097294e-01,
-5.40067692e-01, -6.41509806e-01, -7.41097764e-01,
-7.61821662e-01, -7.07074543e-01, -6.34091830e-01,
-5.23707169e-01, -4.00787037e-01, -2.72040308e-01],
[-6.47319908e+01, -7.04534393e+01, -7.71929758e+01,
-1.04556436e+02, -1.23228444e+02, -1.67668819e+02,
-2.64887092e+02, -3.65137675e+02, -2.58172589e+02,
-1.90585578e+02, -1.48187116e+02, -1.13121625e+02,
-1.09166107e+02, -1.08999523e+02, -1.13778846e+02,
-8.21624174e+01, -1.05634371e+02, -1.28475341e+02,
-1.67408783e+02, -1.63406268e+02, -1.68509356e+02,
-2.42732229e+02, -1.55373912e+02, -9.81262560e+01,
-8.32699616e+01, -8.88243408e+01, -9.14132670e+01,
-1.08043612e+02, -1.26498438e+02, -1.60887899e+02,
-2.52423895e+02, -3.51663241e+02, -2.48154419e+02,
-1.88012759e+02, -1.47321309e+02, -1.11706592e+02,
-1.08499086e+02, -1.08153801e+02, -1.05261025e+02,
-8.46144166e+01, -1.04549875e+02, -1.22646451e+02,
-1.66442507e+02, -1.61099889e+02, -1.66931078e+02,
-2.40034833e+02, -1.43576460e+02, -8.60820143e+01]])array([7, 7, 7])
array(['energy', 'energy', 'energy_per_area', 'energy', 'energy',
'energy_per_area', 'energy_per_area', 'energy', 'energy'],
dtype=object)array([ nan, 0.98 , nan, 0.92685937, nan,
0.405 , 0.92685937, 0.85 , nan, 0.98 ,
nan, nan, 0.92685937, 0.85 , nan,
0.90368789, nan, 0.98 , nan, 0.98 ,
nan, nan, nan, 0.90368789, 0.92685937,
nan])array(['electricity', 'electricity', 'electricity', 'electricity'],
dtype=object)array([25., 25., nan, 25., 25., 25., 25., 25., 25., 25., nan, 25., 25.,
25., nan, 25., nan, 25., nan, 25., 25., 25., nan, 25., 25., 25.])array([1500, 1500, 1500])
array([ 1., 1., nan, 1., 1., 1., 1., 1., 1., 1., nan, 1., 1.,
1., nan, 1., nan, 1., nan, 1., 1., 1., nan, 1., 1., 1.])array([[0.11016807, 0.11016807, 0.11016807, 0.11016807, 0.11016807,
0.11016807, 0.11016807, 0.11016807, 0.11016807, 0.11016807,
0.11016807, 0.11016807, 0.11016807, 0.11016807, 0.11016807,
0.11016807, 0.11016807, 0.11016807, 0.11016807, 0.11016807]])array([[[ nan, nan, nan, nan, nan,
nan, nan, nan, nan, nan,
nan, nan, nan, nan, nan,
nan, nan, nan, nan, nan,
nan, nan, nan, nan, nan,
nan, nan, nan, nan, nan,
nan, nan, nan, nan, nan,
nan, nan, nan, nan, nan,
nan, nan, nan, nan, nan,
nan, nan, nan],
[ nan, nan, nan, nan, nan,
nan, nan, nan, nan, nan,
nan, nan, nan, nan, nan,
nan, nan, nan, nan, nan,
nan, nan, nan, nan, nan,
nan, nan, nan, nan, nan,
nan, nan, nan, nan, nan,
nan, nan, nan, nan, nan,
nan, nan, nan, nan, nan,
nan, nan, nan],
...
[ nan, nan, nan, nan, nan,
nan, nan, nan, nan, nan,
nan, nan, nan, nan, nan,
nan, nan, nan, nan, nan,
nan, nan, nan, nan, nan,
nan, nan, nan, nan, nan,
nan, nan, nan, nan, nan,
nan, nan, nan, nan, nan,
nan, nan, nan, nan, nan,
nan, nan, nan],
[ nan, nan, nan, nan, nan,
nan, nan, nan, nan, nan,
nan, nan, nan, nan, nan,
nan, nan, nan, nan, nan,
nan, nan, nan, nan, nan,
nan, nan, nan, nan, nan,
nan, nan, nan, nan, nan,
nan, nan, nan, nan, nan,
nan, nan, nan, nan, nan,
nan, nan, nan]]])array([[0.1 , 0.004, 0.025, nan, 0.025, nan, 0.004, nan, 0.025]])
array([[ nan, nan, nan, -0.0203, nan, 0.004 , nan,
nan, nan]])array([[ nan, nan, nan, nan, nan, nan, nan, -80.5, nan]])
array([[1.35e+03, 5.00e-02, 9.00e-01, 1.35e+03, 7.50e+02, 9.00e-01,
7.80e+01, 1.35e+03, 1.00e-01, 1.00e+02, 9.00e-01, 4.31e+01,
1.20e+00, 5.00e-02, 1.00e-01, 1.00e+00, 1.00e+00, 1.20e+00,
9.00e-01, 1.00e+00]])array([ 3., 4., 5., 10., 3., 3., 4., 10., 3., 5.])
array(['N1::heat_pipes:X1', 'N1::heat_pipes:X3', 'X3::power_lines:X1',
'X2::power_lines:X1', 'X2::heat_pipes:N1', 'N1::heat_pipes:X2',
'X3::heat_pipes:N1', 'X1::power_lines:X2', 'X1::heat_pipes:N1',
'X1::power_lines:X3'], dtype=object)array([ 500., nan, 900., 1300.])
array([[2, 5, 5, 8],
[7, 7, 3, 7]])array(['#660507', '#8E2999', '#6783E3', '#C5ABE3', '#F9D956', '#E4AB97',
'#072486', '#C98AAD', '#823739'], dtype=object)array(['demand', 'conversion', 'transmission', 'supply',
'supply_power_plus.supply_plus', 'conversion_plus', 'demand',
'supply', 'transmission'], dtype=object)array(['Heat demand', 'Natural gas boiler',
'Electrical power distribution', 'National grid import',
'Solar photovoltaic power', 'Combined heat and power',
'Electrical demand', 'Natural gas import',
'District heat distribution'], dtype=object)array([[1. , 1. , 1. ],
[1. , 1. , 0.8],
[1. , 1. , 1. ]])array(['X3::boiler::gas,X3::supply_gas::gas',
'X3::demand_heat::heat,X3::boiler::heat,X3::heat_pipes:N1::heat',
'X2::pv::electricity,X2::demand_electricity::electricity,X2::power_lines:X1::electricity',
'N1::heat_pipes:X3::heat,N1::heat_pipes:X2::heat,N1::heat_pipes:X1::heat',
'X2::heat_pipes:N1::heat,X2::demand_heat::heat,X2::boiler::heat',
'X1::demand_heat::heat,X1::heat_pipes:N1::heat,X1::chp::heat',
'X1::power_lines:X3::electricity,X1::chp::electricity,X1::demand_electricity::electricity,X1::pv::electricity,X1::supply_grid_power::electricity,X1::power_lines:X2::electricity',
'X2::boiler::gas,X2::supply_gas::gas',
'X3::pv::electricity,X3::power_lines:X1::electricity,X3::demand_electricity::electricity',
'X1::supply_gas::gas,X1::chp::gas'], dtype=object)array(['X1::pv::electricity', 'X1::power_lines:X3::electricity',
'X3::demand_electricity::electricity', 'N1::heat_pipes:X2::heat',
'X2::pv::electricity', 'X2::heat_pipes:N1::heat',
'X3::pv::electricity', 'X2::power_lines:X1::electricity',
'X2::demand_heat::heat', 'X1::supply_grid_power::electricity',
'X1::heat_pipes:N1::heat', 'X2::demand_electricity::electricity',
'X3::heat_pipes:N1::heat', 'X3::demand_heat::heat',
'X3::power_lines:X1::electricity',
'X1::demand_electricity::electricity',
'X1::power_lines:X2::electricity', 'X1::supply_gas::gas',
'X2::supply_gas::gas', 'X1::demand_heat::heat',
'N1::heat_pipes:X3::heat', 'N1::heat_pipes:X1::heat',
'X3::supply_gas::gas'], dtype=object)array([['X3::boiler::gas', 'X2::boiler::gas'],
[None, None],
['X3::boiler::heat', 'X2::boiler::heat']], dtype=object)array(['X1::chp::gas'], dtype=object)
array(['X1::chp::electricity'], dtype=object)
array([['X1::chp::gas'],
['X1::chp::heat'],
['X1::chp::electricity']], dtype=object)array(['X1::pv::electricity', 'X1::chp::electricity',
'X3::pv::electricity', 'X2::pv::electricity'], dtype=object)array(['X1::pv', '', 'X3::pv', 'X2::pv'], dtype=object)
array([1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1.,
1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1.,
1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1.])array([1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1.,
1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1.,
1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1.])array(['2005-07-01T08:00:00.000000000', '2005-07-01T07:00:00.000000000',
'2005-07-01T00:00:00.000000000'], dtype='datetime64[ns]')# Individual data variables can be accessed easily, `to_pandas()` reformats the data to look nicer
model.inputs.resource.to_pandas()
| timesteps | 2005-07-01 00:00:00 | 2005-07-01 01:00:00 | 2005-07-01 02:00:00 | 2005-07-01 03:00:00 | 2005-07-01 04:00:00 | 2005-07-01 05:00:00 | 2005-07-01 06:00:00 | 2005-07-01 07:00:00 | 2005-07-01 08:00:00 | 2005-07-01 09:00:00 | ... | 2005-07-02 14:00:00 | 2005-07-02 15:00:00 | 2005-07-02 16:00:00 | 2005-07-02 17:00:00 | 2005-07-02 18:00:00 | 2005-07-02 19:00:00 | 2005-07-02 20:00:00 | 2005-07-02 21:00:00 | 2005-07-02 22:00:00 | 2005-07-02 23:00:00 |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| loc_techs_finite_resource | |||||||||||||||||||||
| X2::demand_electricity | -94.545801 | -76.960619 | -77.475750 | -77.475750 | -82.731496 | -148.533479 | -189.570817 | -238.734711 | -244.284493 | -231.440181 | ... | -199.895515 | -221.324570 | -188.344877 | -249.962248 | -248.894106 | -269.344347 | -245.412357 | -196.280957 | -135.289242 | -103.741556 |
| X3::demand_heat | -0.015600 | -0.860322 | -0.015600 | -0.015600 | -0.860407 | -7.263327 | -9.398229 | -5.792842 | -3.322585 | -1.927264 | ... | -0.015600 | -0.015600 | -0.860335 | -0.015600 | -0.015600 | -0.860291 | -0.015600 | -0.860300 | -0.015600 | -0.860290 |
| X1::pv | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.007857 | 0.029143 | 0.055571 | 0.078429 | 0.096000 | ... | 0.057857 | 0.041143 | 0.027000 | 0.014714 | 0.006143 | 0.000857 | 0.000000 | 0.000000 | 0.000000 | 0.000000 |
| X1::demand_electricity | -0.455564 | -0.405798 | -0.393291 | -0.393992 | -0.440085 | -0.567821 | -0.732535 | -0.713803 | -0.689992 | -0.707650 | ... | -0.683060 | -0.780370 | -0.940634 | -0.978388 | -1.022063 | -1.169519 | -1.307938 | -1.099334 | -0.826212 | -0.559499 |
| X3::demand_electricity | -18.762912 | -18.762912 | -18.762912 | -18.762912 | -18.762912 | -30.212425 | -35.233307 | -61.395269 | -63.642962 | -62.679665 | ... | -18.762912 | -18.954418 | -19.145924 | -18.762912 | -18.762912 | -18.762912 | -18.762912 | -18.762912 | -18.762912 | -18.762912 |
| X2::pv | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.007857 | 0.029143 | 0.055571 | 0.078429 | 0.096000 | ... | 0.057857 | 0.041143 | 0.027000 | 0.014714 | 0.006143 | 0.000857 | 0.000000 | 0.000000 | 0.000000 | 0.000000 |
| X3::pv | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.007857 | 0.029143 | 0.055571 | 0.078429 | 0.096000 | ... | 0.057857 | 0.041143 | 0.027000 | 0.014714 | 0.006143 | 0.000857 | 0.000000 | 0.000000 | 0.000000 | 0.000000 |
| X1::demand_heat | -0.215376 | -0.200838 | -0.207306 | -0.318949 | -0.650734 | -1.039384 | -1.181567 | -1.285403 | -1.209117 | -1.219912 | ... | -0.491097 | -0.540068 | -0.641510 | -0.741098 | -0.761822 | -0.707075 | -0.634092 | -0.523707 | -0.400787 | -0.272040 |
| X2::demand_heat | -64.731991 | -70.453439 | -77.192976 | -104.556436 | -123.228444 | -167.668819 | -264.887092 | -365.137675 | -258.172589 | -190.585578 | ... | -105.261025 | -84.614417 | -104.549875 | -122.646451 | -166.442507 | -161.099889 | -166.931078 | -240.034833 | -143.576460 | -86.082014 |
9 rows × 48 columns
# To reformat the array, deconcatenating loc_techs / loc_tech_carriers, you can use model.get_formatted_array()
# You can then apply loc/tech/carrier only operations, like summing information over locations:
model.get_formatted_array('resource').sum('locs').to_pandas()
| timesteps | 2005-07-01 00:00:00 | 2005-07-01 01:00:00 | 2005-07-01 02:00:00 | 2005-07-01 03:00:00 | 2005-07-01 04:00:00 | 2005-07-01 05:00:00 | 2005-07-01 06:00:00 | 2005-07-01 07:00:00 | 2005-07-01 08:00:00 | 2005-07-01 09:00:00 | ... | 2005-07-02 14:00:00 | 2005-07-02 15:00:00 | 2005-07-02 16:00:00 | 2005-07-02 17:00:00 | 2005-07-02 18:00:00 | 2005-07-02 19:00:00 | 2005-07-02 20:00:00 | 2005-07-02 21:00:00 | 2005-07-02 22:00:00 | 2005-07-02 23:00:00 |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| techs | |||||||||||||||||||||
| demand_electricity | -113.764277 | -96.129329 | -96.631954 | -96.632654 | -101.934493 | -179.313724 | -225.536658 | -300.843784 | -308.617446 | -294.827496 | ... | -219.341487 | -241.059359 | -208.431436 | -269.703548 | -268.679081 | -289.276777 | -265.483207 | -216.143204 | -154.878366 | -123.063967 |
| demand_heat | -64.962967 | -71.514599 | -77.415882 | -104.890985 | -124.739585 | -175.971530 | -275.466888 | -372.215920 | -262.704292 | -193.732754 | ... | -105.767723 | -85.170084 | -106.051720 | -123.403148 | -167.219928 | -162.667255 | -167.580770 | -241.418840 | -143.992847 | -87.214344 |
| pv | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.023571 | 0.087429 | 0.166714 | 0.235286 | 0.288000 | ... | 0.173571 | 0.123429 | 0.081000 | 0.044143 | 0.018429 | 0.002571 | 0.000000 | 0.000000 | 0.000000 | 0.000000 |
3 rows × 48 columns
# Solve the model. Results are loaded into `model.results`.
# By including logging (see package importing), we can see the timing of parts of the run, as well as the solver's log
model.run()
[2022-02-07 16:53:32] INFO Backend: starting model run [2022-02-07 16:53:33] INFO Loading sets [2022-02-07 16:53:33] INFO Loading parameters [2022-02-07 16:53:33] INFO constraints are loaded in the following order: ['capacity', 'dispatch', 'policy', 'energy_balance', 'costs', 'network', 'conversion', 'group', 'conversion_plus', 'export', 'milp'] [2022-02-07 16:53:33] INFO creating capacity constraints [2022-02-07 16:53:33] INFO creating dispatch constraints [2022-02-07 16:53:33] INFO creating policy constraints [2022-02-07 16:53:33] INFO creating energy_balance constraints [2022-02-07 16:53:33] INFO creating costs constraints [2022-02-07 16:53:33] INFO creating network constraints [2022-02-07 16:53:33] INFO creating conversion constraints [2022-02-07 16:53:33] INFO creating group constraints [2022-02-07 16:53:33] INFO creating conversion_plus constraints [2022-02-07 16:53:33] INFO creating export constraints [2022-02-07 16:53:33] INFO creating milp constraints [2022-02-07 16:53:33] INFO Backend: model generated. Time since start of model run: 0:00:00.506894 [2022-02-07 16:53:33] INFO Backend: sending model to solver [2022-02-07 16:53:33] INFO Backend: solver finished running. Time since start of model run: 0:00:00.897613 [2022-02-07 16:53:33] INFO Backend: loaded results [2022-02-07 16:53:33] INFO Backend: generated solution array. Time since start of model run: 0:00:00.997607 [2022-02-07 16:53:33] INFO Postprocessing: started [2022-02-07 16:53:34] INFO Postprocessing: All values < 1e-10 set to 0 in system_balance [2022-02-07 16:53:34] INFO Postprocessing: ended. Time since start of model run: 0:00:01.158167
# Model results are held in the same structure as model inputs.
# The results consist of the optimal values for all decision variables, including capacities and carrier flow
# There are also results, like system capacity factor and levelised costs, which are calculated in postprocessing
# before being added to the results Dataset
model.results
<xarray.Dataset>
Dimensions: (loc_techs: 26, loc_tech_carriers_prod: 21, timesteps: 48, loc_tech_carriers_con: 19, costs: 1, loc_techs_cost: 20, loc_techs_area: 3, loc_techs_supply_plus: 3, loc_tech_carriers_export: 4, loc_techs_om_cost: 9, loc_techs_investment_cost: 20, loc_carriers: 10, loc_carriers_system_balance_constraint: 10, loc_techs_balance_demand_constraint: 6, loc_techs_cost_investment_constraint: 20, carriers: 3, techs: 16)
Coordinates: (12/17)
* loc_carriers (loc_carriers) object 'N1::heat' ...
* loc_carriers_system_balance_constraint (loc_carriers_system_balance_constraint) object ...
* loc_tech_carriers_con (loc_tech_carriers_con) object 'N...
* loc_tech_carriers_export (loc_tech_carriers_export) object ...
* loc_tech_carriers_prod (loc_tech_carriers_prod) object '...
* loc_techs_balance_demand_constraint (loc_techs_balance_demand_constraint) object ...
... ...
* techs (techs) object 'boiler' ... 'supp...
* carriers (carriers) <U11 'electricity' ......
* costs (costs) object 'monetary'
* loc_techs (loc_techs) object 'X1::pv' ... '...
* loc_techs_area (loc_techs_area) object 'X1::pv' ...
* timesteps (timesteps) datetime64[ns] 2005-0...
Data variables: (12/19)
energy_cap (loc_techs) float64 0.0 ... 0.01835
carrier_prod (loc_tech_carriers_prod, timesteps) float64 ...
carrier_con (loc_tech_carriers_con, timesteps) float64 ...
cost (costs, loc_techs_cost) float64 0...
resource_area (loc_techs_area) float64 0.0 ... ...
resource_con (loc_techs_supply_plus, timesteps) float64 ...
... ...
cost_investment_rhs (costs, loc_techs_cost_investment_constraint) float64 ...
cost_var_rhs (costs, loc_techs_om_cost, timesteps) float64 ...
capacity_factor (timesteps, loc_tech_carriers_prod) float64 ...
systemwide_capacity_factor (carriers, techs) float64 0.0 ......
systemwide_levelised_cost (carriers, costs, techs) float64 ...
total_levelised_cost (carriers, costs) float64 0.08069...
Attributes:
termination_condition: optimal
objective_function_value: 842.3529094335402
solution_time: 0.997607
time_finished: 2022-02-07 16:53:33
calliope_version: 0.6.8-dev
applied_overrides:
scenario: None
defaults: available_area: null\ncarrier_ratios: null\nch...
allow_operate_mode: 1
model_config: calliope_version: 0.6.8\nname: Urban-scale exa...
run_config: backend: pyomo\nbigM: 1000000.0\ncyclic_storag...array(['N1::heat', 'X1::electricity', 'X1::gas', 'X1::heat', 'X2::electricity',
'X2::gas', 'X2::heat', 'X3::electricity', 'X3::gas', 'X3::heat'],
dtype=object)array(['N1::heat', 'X1::electricity', 'X1::gas', 'X1::heat', 'X2::electricity',
'X2::gas', 'X2::heat', 'X3::electricity', 'X3::gas', 'X3::heat'],
dtype=object)array(['N1::heat_pipes:X1::heat', 'N1::heat_pipes:X2::heat',
'N1::heat_pipes:X3::heat', 'X1::chp::gas',
'X1::demand_electricity::electricity', 'X1::demand_heat::heat',
'X1::heat_pipes:N1::heat', 'X1::power_lines:X2::electricity',
'X1::power_lines:X3::electricity', 'X2::boiler::gas',
'X2::demand_electricity::electricity', 'X2::demand_heat::heat',
'X2::heat_pipes:N1::heat', 'X2::power_lines:X1::electricity',
'X3::boiler::gas', 'X3::demand_electricity::electricity',
'X3::demand_heat::heat', 'X3::heat_pipes:N1::heat',
'X3::power_lines:X1::electricity'], dtype=object)array(['X1::chp::electricity', 'X1::pv::electricity', 'X2::pv::electricity',
'X3::pv::electricity'], dtype=object)array(['N1::heat_pipes:X1::heat', 'N1::heat_pipes:X2::heat',
'N1::heat_pipes:X3::heat', 'X1::chp::electricity', 'X1::chp::heat',
'X1::heat_pipes:N1::heat', 'X1::power_lines:X2::electricity',
'X1::power_lines:X3::electricity', 'X1::pv::electricity',
'X1::supply_gas::gas', 'X1::supply_grid_power::electricity',
'X2::boiler::heat', 'X2::heat_pipes:N1::heat',
'X2::power_lines:X1::electricity', 'X2::pv::electricity',
'X2::supply_gas::gas', 'X3::boiler::heat', 'X3::heat_pipes:N1::heat',
'X3::power_lines:X1::electricity', 'X3::pv::electricity',
'X3::supply_gas::gas'], dtype=object)array(['X1::demand_electricity', 'X1::demand_heat', 'X2::demand_electricity',
'X2::demand_heat', 'X3::demand_electricity', 'X3::demand_heat'],
dtype=object)array(['N1::heat_pipes:X1', 'N1::heat_pipes:X2', 'N1::heat_pipes:X3',
'X1::chp', 'X1::heat_pipes:N1', 'X1::power_lines:X2',
'X1::power_lines:X3', 'X1::pv', 'X1::supply_gas',
'X1::supply_grid_power', 'X2::boiler', 'X2::heat_pipes:N1',
'X2::power_lines:X1', 'X2::pv', 'X2::supply_gas', 'X3::boiler',
'X3::heat_pipes:N1', 'X3::power_lines:X1', 'X3::pv', 'X3::supply_gas'],
dtype=object)array(['N1::heat_pipes:X1', 'N1::heat_pipes:X2', 'N1::heat_pipes:X3',
'X1::chp', 'X1::heat_pipes:N1', 'X1::power_lines:X2',
'X1::power_lines:X3', 'X1::pv', 'X1::supply_gas',
'X1::supply_grid_power', 'X2::boiler', 'X2::heat_pipes:N1',
'X2::power_lines:X1', 'X2::pv', 'X2::supply_gas', 'X3::boiler',
'X3::heat_pipes:N1', 'X3::power_lines:X1', 'X3::pv', 'X3::supply_gas'],
dtype=object)array(['N1::heat_pipes:X1', 'N1::heat_pipes:X2', 'N1::heat_pipes:X3',
'X1::chp', 'X1::heat_pipes:N1', 'X1::power_lines:X2',
'X1::power_lines:X3', 'X1::pv', 'X1::supply_gas',
'X1::supply_grid_power', 'X2::boiler', 'X2::heat_pipes:N1',
'X2::power_lines:X1', 'X2::pv', 'X2::supply_gas', 'X3::boiler',
'X3::heat_pipes:N1', 'X3::power_lines:X1', 'X3::pv', 'X3::supply_gas'],
dtype=object)array(['X1::chp', 'X1::supply_gas', 'X1::supply_grid_power', 'X2::boiler',
'X2::pv', 'X2::supply_gas', 'X3::boiler', 'X3::pv', 'X3::supply_gas'],
dtype=object)array(['X1::pv', 'X2::pv', 'X3::pv'], dtype=object)
array(['boiler', 'chp', 'demand_electricity', 'demand_heat', 'heat_pipes',
'heat_pipes:N1', 'heat_pipes:X1', 'heat_pipes:X2', 'heat_pipes:X3',
'power_lines', 'power_lines:X1', 'power_lines:X2', 'power_lines:X3',
'pv', 'supply_gas', 'supply_grid_power'], dtype=object)array(['electricity', 'gas', 'heat'], dtype='<U11')
array(['monetary'], dtype=object)
array(['X1::pv', 'X1::power_lines:X3', 'X3::demand_electricity',
'N1::heat_pipes:X2', 'X2::pv', 'X1::chp', 'X2::heat_pipes:N1',
'X3::boiler', 'X3::pv', 'X2::power_lines:X1', 'X2::demand_heat',
'X1::supply_grid_power', 'X1::heat_pipes:N1', 'X2::boiler',
'X2::demand_electricity', 'X3::heat_pipes:N1', 'X3::demand_heat',
'X3::power_lines:X1', 'X1::demand_electricity', 'X1::power_lines:X2',
'X1::supply_gas', 'X2::supply_gas', 'X1::demand_heat',
'N1::heat_pipes:X3', 'N1::heat_pipes:X1', 'X3::supply_gas'],
dtype=object)array(['X1::pv', 'X3::pv', 'X2::pv'], dtype=object)
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0. ],
[0.15375983, 0.58850391, nan, nan, nan,
0.24806143, 0.35278664, 0.01369202, 0.01930144, nan,
0. , 0. , 0. , 0. , 0. ,
0. ]])array([[[ inf, 1.68219339e-02, nan, nan,
nan, inf, inf, inf,
inf, nan, 9.78666175e-07, inf,
1.29274001e-05, 4.48962739e-02, inf, 1.15972428e-01]],
[[ inf, inf, nan, nan,
nan, inf, inf, inf,
inf, nan, inf, inf,
inf, inf, 2.50215310e-02, inf]],
[[8.23133186e-03, 2.10274174e-02, nan, nan,
nan, 2.29130409e-05, 1.60417599e-05, 4.13329737e-04,
3.90942807e-04, nan, inf, inf,
inf, inf, inf, inf]]])array([[0.08069267],
[0.0344869 ],
[0.09578106]])# We can sum heat output over all locations and turn the result into a pandas DataFrame
df_heat = model.get_formatted_array('carrier_prod').loc[{'carriers':'heat'}].sum('locs').to_pandas().T
#The information about the dataframe tells us about the amount of data it holds in the index and in each column
df_heat.info()
<class 'pandas.core.frame.DataFrame'> DatetimeIndex: 48 entries, 2005-07-01 00:00:00 to 2005-07-02 23:00:00 Data columns (total 12 columns): # Column Non-Null Count Dtype --- ------ -------------- ----- 0 boiler 48 non-null float64 1 chp 48 non-null float64 2 heat_pipes:N1 48 non-null float64 3 heat_pipes:X1 48 non-null float64 4 heat_pipes:X2 48 non-null float64 5 heat_pipes:X3 48 non-null float64 6 power_lines:X1 48 non-null float64 7 power_lines:X2 48 non-null float64 8 power_lines:X3 48 non-null float64 9 pv 48 non-null float64 10 supply_gas 48 non-null float64 11 supply_grid_power 48 non-null float64 dtypes: float64(12) memory usage: 5.9 KB
# Using .head() to see the first few rows of heat generation and demand
# Note: carrier production in heat_pipes:N1 is heat received by the heat network at any location connected to `N1`
df_heat.head()
| techs | boiler | chp | heat_pipes:N1 | heat_pipes:X1 | heat_pipes:X2 | heat_pipes:X3 | power_lines:X1 | power_lines:X2 | power_lines:X3 | pv | supply_gas | supply_grid_power |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| timesteps | ||||||||||||
| 2005-07-01 00:00:00 | 0.000000 | 92.861360 | 170.055601 | 183.475520 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 |
| 2005-07-01 01:00:00 | 4.100046 | 78.466297 | 67.213715 | 72.541074 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 |
| 2005-07-01 02:00:00 | 9.626102 | 78.876806 | 67.582474 | 72.915564 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 |
| 2005-07-01 03:00:00 | 37.084989 | 78.877367 | 67.487047 | 72.812606 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 |
| 2005-07-01 04:00:00 | 53.191064 | 83.204646 | 70.897788 | 76.515868 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 |
# We can plot this by using the timeseries plotting functionality.
# The top-left dropdown gives us the chance to scroll through other timeseries data too.
model.plot.timeseries()
[2022-02-07 16:53:35] WARNING /Users/brynmorp/Repos/calliope-project/calliope/calliope/postprocess/plotting/plotting.py:105: FutureWarning: Plotting will no longer be available as a method of the Calliope model object infuture versions of Calliope. In the meantime, as of v0.6.6, plotting is untested; this functionality should now be used with caution. We expect to reintroduce it as a seperate module in v0.7.0.